采样频率影响心房颤动检测效果，中等频率表现最佳。
Sampling Matters: The Effect of ECG Frequency on Deep Learning-Based Atrial Fibrillation Detection
- 通过不同采样率重采样数据，测试两种模型在12导联心电图上的表现
- 100-250Hz时混合模型性能最优，500Hz下传统CNN准确率显著下降
- 提醒未来心律失常模型需明确控制时间分辨率以保证临床可靠性
用于心房颤动（AF）检测的深度学习模型越来越多地在具有不同采样频率的异构心电图（ECG）数据集上训练，但这些差异对模型性能、校准和鲁棒性的具体影响尚未充分阐明。为此，我们使用来自PTB-XL数据集的12导联、10秒心电图记录，将其重采样至62、100、250和500 Hz，评估标准一维卷积神经网络（1-D CNN）和混合CNN-LSTM架构在严格的患者安全交叉验证框架下的表现。分析显示，采样频率显著影响检测指标，且具有架构依赖性：混合CNN-LSTM模型在中等频率（100-250 Hz）下表现最优且校准一致；而1-D CNN基线模型在500 Hz时准确率与敏感性明显下降，表明对高频噪声更敏感。结论指出，心电图采样频率是心律失常检测中一个关键但被忽视的因素，未来基础模型必须明确控制时间分辨率以确保临床可靠性与可复现性。
原文摘要 · Abstract (English)
Deep learning models for atrial fibrillation (AF) detection are increasingly trained on heterogeneous electrocardiogram (ECG) datasets with varying sampling frequencies, yet the specific consequences of these discrepancies on model performance, calibration, and robustness remain insufficiently characterized. To address this, we conducted a systematic benchmark using 12-lead, 10-second recordings from the PTB-XL dataset, resampled to target frequencies of 62, 100, 250, and 500 Hz, to evaluate a standard 1-D Convolutional Neural Network (CNN) and a hybrid CNN-Long Short-Term Memory (LSTM) architecture under a rigorous patient-safe cross-validation framework. Our analysis reveals that sampling frequency significantly impacts detection metrics in an architecture-dependent manner; the hybrid CNN-LSTM model demonstrated optimal performance and consistent calibration at intermediate frequencies (100-250 Hz), whereas the 1-D CNN baseline exhibited marked degradation in accuracy and sensitivity at 500 Hz, suggesting increased susceptibility to high-frequency noise. We conclude that ECG sampling frequency is a critical, underappreciated factor in arrhythmia detection, and future foundation models must explicitly control for temporal resolution to ensure clinical reliability and reproducibility.
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